Observation or surveillance cameras are installed almost everywhere, but especially in areas where services are provided to customers. Just to give a few examples, the cameras transmit images from train stations, airports, streets or supermarkets. The images help the service providers to increase the quality of service and to sustain security. The providers monitor the images on screens in real time and/or watch offline recordings. Recordings are especially useful to investigate incidents. The terms “observation” and “surveillance” have slightly different meaning, with “surveillance” having a connotation towards safety and security. But in the following description, both terms are used as synonyms.
The lenses of the camera expose light to a sensor that periodically reads out the images. Consecutive images are called frames. The camera provides new frames in a time interval that is called “frame time”. The light exposure time can be shorter than the frame time. In an alternative wording, the term “frame rate” indicates the number of frames per second (FPS). Depending on the frame rate, the images appear as a motion picture (or video, approximately 20 FPS or more), or appear as stand-still pictures. In principle, a camera catches every object that is in the field of view (FOV).
However, many usage scenarios deal with objects that are moving, such as passengers with or without luggage trolleys (e.g., in train stations or airports), or shoppers (e.g., in markets). Usually, multiple persons are being monitored. When waiting at service counters, the persons may line up in queues. In case that queues are getting longer, the service providers consider opening new service counters to decrease waiting times. However, situations might escalate to security hazards and event incidents when a queue turns into a crowd.
For service providers and security experts it is therefore of interest to know the properties of a queue—or queue characteristics—such as the number of persons in the queue, the waiting time it takes them to reach the counter, the shape of the queue, or the like.
Even for a person in the queue, some information is desirable to know, especially the waiting time.
The providers could identify some queue characteristics by having a dedicated operator constantly looking at the screen. The operator could count the persons in the queue, could trace a particular person to guess the waiting time, and so on. But usually, the operator looks at many screens simultaneously. Further, the operator has to pay attention to potential incidents so that manually determining the queue characteristics can not be justified.
However, calculating queue characteristics by computers faces constraints. This seems to be especially true for queues with persons. Queues that follow straight lines are quite exceptional. Queues tend to have irregular shapes. Persons in the queue move at different speed, and some persons leave the queue before reaching the target (leave-takers). Some persons just pass through the queue without joining. Other persons might accompany queuing people, but do not need to use the service counter. When reaching the counter, some people receive services in short time, some need more time. In some countries, individual persons in a queue should not be identified for data protection reasons.
Hence, there is a need to improve computer techniques for estimating movement characteristics of objects in queues.